Classwork 8

Map Visualization

Loading R packages

library(tidyverse)
library(skimr)
library(ggthemes)
library(socviz)
library(geofacet)

Part 1. Climate Opinion Map

The following data is for Part 1:

climate_opinion_long <- read_csv(
  'https://bcdanl.github.io/data/climate_opinion_2021.csv')

Variable Description

• belief:
  • human: Estimated percentage who think that global warming is caused mostly by human activities.
  • happening: Estimated percentage who think that global warming is happening.

Question 1

• Filter climate_opinion_long, so that climate_opinion_long has only estimated percentage of people who think that global warming is caused mostly by human activities.

climate_opinion_long <- climate_opinion_long |>  
  filter(belief == 'human')

Question 2

• Join the two data.frames, socviz::county_map and the resulting data.frame in Question 1.

county_map <- socviz::county_map

county_map <- county_map |> 
  mutate(id = as.integer(id))

county_full <- county_map |> 
  left_join(climate_opinion_long)

Question 3

• Replicate the following map.
  • Do not use coord_map(projection = "albers", lat0 = 39, lat1 = 45).

qtiles <- quantile(climate_opinion_long$perc, 
                   probs = c(0, 0.25, 0.5, 0.75, 1), 
                   na.rm = TRUE)

brk <- as.numeric(qtiles)

lab <- paste0(round(qtiles, 1), 
              "\n(", 
              c("Min", "25th", "50th", "75th", "Max"), 
              ")")

p1 <- ggplot(data = county_full) + 
  geom_polygon(mapping = aes(x = long, y = lat, group = group, 
                             fill = perc),
               color = "grey60", 
               linewidth = 0.1) 

p2 <- p1 + scale_fill_gradient2( 
  low = '#2E74C0',  
  high = '#CB454A',  
  mid = 'white', 
  na.value = "grey80",
  midpoint = quantile(climate_opinion_long$perc, .5, na.rm = T),
  breaks = brk,
  labels = lab,
  guide = guide_colorbar( direction = "horizontal",
                          barwidth = 25,
                          title.vjust = 1 )
) 

p <- p2 + labs(fill = "Percent\nBelief", title = "U.S. Climate Opinion, 2021",
               caption = "Sources: Yale Program on Climate Change Communication\n(https://climatecommunication.yale.edu/visualizations-data/ycom-us/)") +
  theme_map() +
  theme(plot.margin = unit( c(1, 1, 3.85, 0.5), "cm"),
        plot.title = element_text(size = rel(2),
                                  hjust = .5),
        plot.caption = element_text(hjust = 1),
        legend.position = c(0.5, -.15),
        legend.justification = c(.5,.5),
        aspect.ratio = .8,
        strip.background = element_rect( colour = "black",
                                         fill = "white",
                                         color = "grey80" )
  ) +
  guides(fill = guide_colourbar(direction = "horizontal", 
                                barwidth = 25,
                                title.vjust = 1)
         )
p

Part 2. Unemployment Rate Maps with geofacet::facet_geo()

The following data is for Part 2:

unemp_house_prices <- read_csv(
  'https://bcdanl.github.io/data/unemp_house_prices.csv')

• Use geom_area(), geom_line(), and facet_geo(~state, labeller = adjust_labels) to replicate the following figure

adjust_labels <- as_labeller(
  function(x) {
    case_when(
      x == "New Hampshire" ~ "N. Hampshire",
      x == "District of Columbia" ~ "DC",
      TRUE ~ x
    )
  }
)

unemp_house_prices |> 
  filter(
    date >= ymd("2008-01-01")
  ) |>
  ggplot(aes(date, unemploy_perc)) + 
  geom_area(fill = "#56B4E9", alpha = 0.7) +
  geom_line() + 
  scale_y_continuous(
    name = "unemployment rate",
    limits = c(0, 16),
    breaks = c(0, 5, 10, 15),
    labels = c("0%", "5%", "10%", "15%")
  ) +
  scale_x_date(
    name = NULL,
    breaks = ymd(c("2009-01-01", "2011-01-01", 
                   "2013-01-01", "2015-01-01", "2017-01-01")),
    labels = c("'09", "'11", "'13", "'15", "'17")
  ) +
  facet_geo(~state, labeller = adjust_labels) +
  theme(
    strip.text = element_text(
      margin = margin(3, 3, 3, 3)
    ),
    axis.line.x = element_blank()
  )

Discussion

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